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ORCIDYong Zheng 0001
Person information
- affiliation: Illinois Institute of Technology, College of Computing, Chicago, IL, USA
- affiliation: DePaul University, Chicago, IL, USA
Other persons with the same name
- Yong Zheng — disambiguation page
- Yong Zheng 0002
![[0000-0001-8569-2680]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-8569-2680")
— Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China (and 1 more) - Yong Zheng 0003 — Chongqing University of Technology, Engineering Research Center of Mechanical Testing Technology and Equipment, China (and 1 more)
- Yong Zheng 0004 — Shandong Provincial Agricultural Information Center, Ji'nan, China
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2020 – today
- 2023
[j11]Yong Zheng, Kumar Neelotpal Shukla, Jasmine Xu, David (Xuejun) Wang, Michael O'Leary:
MOPO-LSI: An open-source multi-objective portfolio optimization library for sustainable investments. Softw. Impacts 16: 100499 (2023)- [c75]Yong Zheng:
Tutorial: Educational Recommender Systems. AIED (Posters/Late Breaking Results/...) 2023: 50-56 - [c74]Yong Zheng, Kumar Neelotpal Shukla, Jasmine Xu, David Xuejun Wang, Michael O'Leary:
Multi-Objective Portfolio Optimization Towards Sustainable Investments. COMPASS 2023: 124-128 - [c73]Yong Zheng, David (Xuejun) Wang:
Multi-Criteria Decision Making and Recommender Systems. IUI Companion 2023: 181-184 - [c72]Yong Zheng, David Xuejun Wang:
A Comparative Study of Preference Ordering Methods for Multi-Criteria Ranking. SDS 2023: 108-111 - [c71]Yong Zheng:
ChatGPT for Teaching and Learning: An Experience from Data Science Education. SIGITE 2023: 66-72 - [c70]Yong Zheng:
Academic Writing by Using Latex: A Hands-on Workshop. SIGITE 2023: 90-91 - [c69]Yong Zheng, David Xuejun Wang:
Multi-Criteria Ranking by Using Relaxed Pareto Ranking Methods. UMAP (Adjunct Publication) 2023: 81-85 - [i9]Yong Zheng:
ITM-Rec: An Open Data Set for Educational Recommender Systems. CoRR abs/2303.10230 (2023) - [i8]Yong Zheng, David Xuejun Wang:
Hybrid Multi-Criteria Preference Ranking by Subsorting. CoRR abs/2306.11233 (2023) - [i7]Yong Zheng, Kumar Neelotpal Shukla, Jasmine Xu, David (Xuejun) Wang, Michael O'Leary:
MOPO-LSI: A User Guide. CoRR abs/2307.01719 (2023) - [i6]Yong Zheng:
ChatGPT for Teaching and Learning: An Experience from Data Science Education. CoRR abs/2307.16650 (2023) - 2022
- [j10]Yong Zheng, David (Xuejun) Wang:
Multi-Criteria Ranking: Next Generation of Multi-Criteria Recommendation Framework. IEEE Access 10: 90715-90725 (2022) - [j9]Yong Zheng:
Non-dominated differential context modeling for context-aware recommendations. Appl. Intell. 52(9): 10008-10021 (2022) - [j8]Yong Zheng, David (Xuejun) Wang:
A survey of recommender systems with multi-objective optimization. Neurocomputing 474: 141-153 (2022) - [j7]Yong Zheng:
Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison. Inf. 13(1): 42 (2022) - [j6]Yong Zheng, Li Chen, Markus Zanker, Panagiotis Symeonidis:
JIIS preface for the special issue on advances in recommender systems. J. Intell. Inf. Syst. 58(2): 223-225 (2022) - [j5]Yong Zheng:
DeepCARSKit: A deep learning based context-aware recommendation library. Softw. Impacts 13: 100292 (2022) - [c68]Toine Bogers, Cataldo Musto, David (Xuejun) Wang, Alexander Felfernig, Simone Borg Bruun, Giovanni Semeraro, Yong Zheng:
FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services. RecSys 2022: 688-690 - [c67]Yong Zheng, Shuaiqi Zheng:
A Comparison of Students' Learning Behaviors and Performance Among Pre, During and Post COVID-19 Pandemic. SIGITE 2022: 78-85 - [c66]Yong Zheng, Arnold Liu, Shuaiqi Zheng:
Pressure Test: Finding Appropriate Data Size for Practice in Data Science Education. SIGITE 2022: 142-149 - [c65]Yong Zheng, Gonzalo Florez Arias:
A Family of Neural Contextual Matrix Factorization Models for Context-Aware Recommendations. UMAP (Adjunct Publication) 2022: 1-6 - [c64]Yong Zheng:
DeepCARSKit: A Demo and User Guide. UMAP (Adjunct Publication) 2022: 18-21 - [e2]Ray Trygstad, Yong Zheng:
SIGITE '22: The 23rd Annual Conference on Information Technology Education, Chicago, IL, USA, September 21 - 24, 2022. ACM 2022, ISBN 978-1-4503-9391-1 [contents] - 2021
- [j4]Yong Zheng, Archana Subramaniyan:
Personality-aware recommendations: an empirical study in education. Int. J. Grid Util. Comput. 12(5/6): 524-533 (2021) - [j3]Yong Zheng, Juan Ruiz Toribio:
The role of transparency in multi-stakeholder educational recommendations. User Model. User Adapt. Interact. 31(3): 513-540 (2021) - [c63]Yong Zheng, David (Xuejun) Wang:
Multi-Objective Recommendations. KDD 2021: 4098-4099 - [i5]Yong Zheng, David (Xuejun) Wang:
Multi-Objective Recommendations: A Tutorial. CoRR abs/2108.06367 (2021) - 2020
- [j2]Yong Zheng:
Penalty-Enhanced Utility-Based Multi-Criteria Recommendations. Inf. 11(12): 551 (2020) - [c62]Yong Zheng:
Educational Group Recommendations with Virtual Leaders. ICALT 2020: 52-56 - [c61]Yong Zheng:
SIGITE and SIGCSE Symposiums: A Comparative Study. SIGITE 2020: 132-137 - [c60]Yong Zheng:
Fill Students' Knowledge Gap by Recommending Remedial Learning Materials. SIGITE 2020: 217 - [c59]Alejandro Susillo Ridao, Yong Zheng:
A Comparison of Tools and Libraries for In-Class Face Detection and Emotion Recognition. SIGITE 2020: 295 - [c58]Vih Modeste Dogo, Khushboo Garg, Yong Zheng:
Ride-Share Analysis in Boston City: A Comparison Between Uber and Lyft. SIGITE 2020: 348 - [c57]Yong Zheng, Shubham Madke, Vih Modeste Dogo, Yash Agrawal:
Predictive Staffing Model for Transit Security. SIGITE 2020: 349 - [i4]Diego Sánchez-Moreno, Yong Zheng, María N. Moreno García:
Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach. CoRR abs/2008.11432 (2020)
2010 – 2019
- 2019
- [j1]Yong Zheng:
Preference corrections: capturing student and instructor perceptions in educational recommendations. Smart Learn. Environ. 6(1): 29 (2019) - [c56]Yong Zheng, Sridhar Srinivasan:
Mobile App and Malware Classifications by Mobile Usage with Time Dynamics. AINA 2019: 595-606 - [c55]Yong Zheng, Archana Subramaniyan:
Personality-Aware Collaborative Learning: Models and Explanations. AINA 2019: 631-642 - [c54]Muthusamy Chelliah, Yong Zheng, Sudeshna Sarkar:
Recommendation for Multi-stakeholders and through Neural Review Mining. CIKM 2019: 2979-2981 - [c53]Yong Zheng:
Multi-stakeholder Personalized Learning with Preference Corrections. ICALT 2019: 66-70 - [c52]Yong Zheng:
Multi-stakeholder recommendations: case studies, methods and challenges. RecSys 2019: 578-579 - [c51]Yong Zheng, Shephalika Shekhar, Alisha Anna Jose, Sunil Kumar Rai:
Integrating context-awareness and multi-criteria decision making in educational learning. SAC 2019: 2453-2460 - [c50]Yong Zheng, Alisha Anna Jose:
Context-aware recommendations via sequential predictions. SAC 2019: 2525-2528 - [c49]Yong Zheng:
Utility-based multi-criteria recommender systems. SAC 2019: 2529-2531 - [c48]Yong Zheng:
A Course on Applied AI and Data Science: Recommender Systems. SIGITE 2019: 43-48 - [c47]Geeth Priya Kuppusamy, Chandni Thakkar, Yong Zheng:
Bikesharing: Analysis and Prediction. SIGITE 2019: 157 - [c46]Nikita Shahane, Megha Kaul, Yong Zheng:
Exploratory Analysis of Chicago Taxi Rides. SIGITE 2019: 158 - [c45]Yong Zheng:
A Comparison of Tools for Teaching and Learning Data Analytics. SIGITE 2019: 160 - [c44]Yong Zheng:
Educational Group Recommendations By Learning Group Expectations. TALE 2019: 1-7 - [c43]Yong Zheng, Nastaran Ghane, Milad Sabouri:
Personalized Educational Learning with Multi-Stakeholder Optimizations. UMAP (Adjunct Publication) 2019: 283-289 - 2018
- [c42]Nadia Najjar, Yong Zheng:
Special Track on Recommender Systems. FLAIRS 2018: 455 - [c41]Yong Zheng:
Context-Aware Mobile Recommendation By A Novel Post-Filtering Approach. FLAIRS 2018: 482-485 - [c40]Yong Zheng:
Personality-Aware Decision Making In Educational Learning. IUI Companion 2018: 58:1-58:2 - [c39]Yong Zheng:
Identifying Dominators and Followers in Group Decision Making based on The Personality Traits. IUI Workshops 2018 - [c38]Shaghayegh (Sherry) Sahebi, Yong Zheng, Weike Pan, Ignacio Fernández:
The 2nd workshop on intelligent recommender systems by knowledge transfer & learning (recsysKTL). RecSys 2018: 523-524 - [c37]Yong Zheng, Mengran Liu:
Predicting Political Trends By Social Tagging. SIGITE 2018: 157 - [c36]Yong Zheng, Sridhar Srinivasan, Taehun Kim:
Exploratory Malware Analysis of Mobile Usages. SIGITE 2018: 158 - [c35]Yong Zheng, Tanaya Dave, Neha Mishra, Harshit Kumar:
Fairness In Reciprocal Recommendations: A Speed-Dating Study. UMAP (Adjunct Publication) 2018: 29-34 - [c34]Yong Zheng:
Exploring User Roles In Group Recommendations: A Learning Approach. UMAP (Adjunct Publication) 2018: 49-52 - [c33]Yong Zheng, Aviana Pu:
Utility-Based Multi-Stakeholder Recommendations by Multi-Objective Optimization. WI 2018: 128-135 - [c32]Diego Sánchez-Moreno, Yong Zheng, María N. Moreno García:
Incorporating Time Dynamics and Implicit Feedback into Music Recommender Systems. WI 2018: 580-585 - [p2]Yong Zheng, Bamshad Mobasher:
Context-Aware Recommendations. Collaborative Recommendations 2018: 173-202 - 2017
- [c31]Yong Zheng, Mayur Agnani, Mili Singh:
Identification of Grey Sheep Users by Histogram Intersection in Recommender Systems. ADMA 2017: 148-161 - [c30]Yong Zheng:
Criteria Chains: A Novel Multi-Criteria Recommendation Approach. IUI 2017: 29-33 - [c29]Yong Zheng, Weike Pan, Shaghayegh (Sherry) Sahebi, Ignacio Fernández:
The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL). RecSys 2017: 366-367 - [c28]Yong Zheng, Mayur Agnani, Mili Singh:
Identifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filtering. RITT 2017: 1-6 - [c27]Mandar Kadam, Nisha Kanoo, Yong Zheng:
Climate Change: Relationships to CO2 Emission and Locations. RITT 2017: 35 - [c26]Yong Zheng:
Situation-aware multi-criteria recommender system: using criteria preferences as contexts. SAC 2017: 689-692 - [c25]Yong Zheng:
Indirect Context Suggestion. UMAP 2017: 399-400 - [c24]Yong Zheng:
Context suggestion: empirical evaluations vs user studies. WI 2017: 753-760 - [c23]Yong Zheng:
Affective prediction by collaborative chains in movie recommendation. WI 2017: 815-822 - [p1]Yong Zheng, Bamshad Mobasher, Robin Burke:
Emotions in Context-Aware Recommender Systems. Emotions and Personality in Personalized Services 2017: 311-326 - [e1]Yong Zheng, Weike Pan, Shaghayegh (Sherry) Sahebi, Ignacio Fernández:
Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning co-located with ACM Conference on Recommender Systems (RecSys 2017), Como, Italy, August 27, 2017. CEUR Workshop Proceedings 1887, CEUR-WS.org 2017 [contents] - [i3]Yong Zheng:
Multi-Stakeholder Recommendation: Applications and Challenges. CoRR abs/1707.08913 (2017) - [i2]Yong Zheng:
Interpreting Contextual Effects By Contextual Modeling In Recommender Systems. CoRR abs/1710.08516 (2017) - 2016
- [c22]Yong Zheng:
Adapt to Emotional Reactions in Context-aware Personalization. EMPIRE@RecSys 2016: 1-8 - [c21]Yong Zheng:
Context-driven mobile apps management and recommendation: student research abstract. SAC 2016: 633-634 - [c20]Yong Zheng, Bamshad Mobasher, Robin Burke:
User-Oriented Context Suggestion. UMAP 2016: 249-258 - 2015
- [c19]Yong Zheng:
Context Suggestion: Solutions and Challenges. ICDM Workshops 2015: 1602-1603 - [c18]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
CARSKit: A Java-Based Context-Aware Recommendation Engine. ICDM Workshops 2015: 1668-1671 - [c17]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Incorporating Context Correlation into Context-aware Matrix Factorization. CPCR+ITWP@IJCAI 2015 - [c16]Yong Zheng:
A Revisit to The Identification of Contexts in Recommender Systems. IUI Companion 2015: 133-136 - [c15]Yong Zheng:
Improve general contextual slim recommendation algorithms by factorizing contexts. SAC 2015: 929-930 - [c14]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Integrating Context Similarity with Sparse Linear Recommendation Model. UMAP 2015: 370-376 - [c13]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Similarity-Based Context-Aware Recommendation. WISE (1) 2015: 431-447 - [i1]Yong Zheng:
A User's Guide to CARSKit. CoRR abs/1511.03780 (2015) - 2014
- [c12]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Deviation-Based Contextual SLIM Recommenders. CIKM 2014: 271-280 - [c11]Zhichun Li, Yong Zheng:
Seismic Qualification of Telecommunication System in Nuclear Power Plant. ICGEC 2014: 353-358 - [c10]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
CSLIM: contextual SLIM recommendation algorithms. RecSys 2014: 301-304 - [c9]Yong Zheng:
Deviation-based and similarity-based contextual SLIM recommendation algorithms. RecSys 2014: 437-440 - [c8]Yong Zheng, Robin D. Burke, Bamshad Mobasher:
Splitting approaches for context-aware recommendation: an empirical study. SAC 2014: 274-279 - [c7]Yong Zheng:
Semi-supervised context-aware matrix factorization: using contexts in a way of "latent" factors. SAC 2014: 292-293 - [c6]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
Context Recommendation Using Multi-label Classification. WI-IAT (1) 2014: 288-295 - 2013
- [c5]Yong Zheng, Bamshad Mobasher, Robin D. Burke:
The Role of Emotions in Context-aware Recommendation. Decisions@RecSys 2013: 21-28 - [c4]Yong Zheng, Robin D. Burke, Bamshad Mobasher:
Recommendation with Differential Context Weighting. UMAP 2013: 152-164 - 2012
- [c3]Yong Zheng, Robin D. Burke, Bamshad Mobasher:
Differential Context Relaxation for Context-Aware Travel Recommendation. EC-Web 2012: 88-99 - 2011
- [c2]Negar Hariri, Bamshad Mobasher, Robin Burke, Yong Zheng:
Context-Aware Recommendation Based On Review Mining. ITWP@IJCAI 2011 - [c1]Robin Burke, Yong Zheng, Scott Riley:
Experience Discovery: hybrid recommendation of student activities using social network data. HetRec@RecSys 2011: 49-52
Coauthor Index
Robin D. Burke
aka: Robin Burke
aka: Robin Burke
David (Xuejun) Wang
aka: David Xuejun Wang
aka: David Xuejun Wang
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